Apparatus of machine learning, machine learning method, and inference apparatus

Abstract

An apparatus of machine learning includes processing circuitry. The processing circuitry uses a first calibration model that receives, as input, first processing data and a first processing label assigned by a user to the first processing data and outputs calibration data relating to calibration of individual characteristics in label assignment by the first user, and trains a target model based on at least the first processing data and the calibration data or a calibrated label having individual characteristics calibrated using the calibration data.

1 . An apparatus of machine learning comprising:
 processing circuitry configured to train, by using a first calibration model that receives, as input, first processing data and a first processing label assigned by a first user to the first processing data, and outputs calibration data relating to calibration of individual characteristics in label assignment by the first user, a target model based on at least the first processing data and the calibration data or a calibration label having individual characteristics calibrated using the calibration data.   
     
     
         2 . The apparatus of machine learning according to  claim 1 , wherein the processing circuitry is configured to:
 generate, as the calibration data, a calibration parameter with respect to the first processing label by applying the first calibration model to the first processing data and the first processing label;   generate the calibration label by applying the calibration parameter to the first processing label; and   train the target model based on the first processing data and the first calibration label.   
     
     
         3 . The apparatus of machine learning according to  claim 1 , wherein the processing circuitry is configured to:
 generate, as the calibration data, the calibration label by applying the first calibration model to the first processing data and the first processing label; and   train the target model based on the first processing data and the calibration label.   
     
     
         4 . The apparatus of machine learning according to  claim 1 , wherein the processing circuitry is configured to:
 output, as the calibration data, a reliability with respect to the first processing label by applying the first calibration model to the first processing data and the first processing label; and   train the target model based on the first processing data and the first processing label by using the reliability as a parameter of a loss function.   
     
     
         5 . The apparatus of machine learning according to  claim 1 , wherein the processing circuitry is configured to generate the first calibration model based on first input training data, a first training label assigned by the first user to the first input training data, and the calibration data. 
     
     
         6 . The apparatus of machine learning according to  claim 5 , wherein the processing circuitry is configured to assign the first training label to the first input training data in accordance with an instruction by the first user. 
     
     
         7 . The apparatus of machine learning according to  claim 5 , wherein the first input training data is medical data generated by a medical device. 
     
     
         8 . The apparatus of machine learning according to  claim 5 , wherein the first input training data is an MR image in which a measurement voxel by MR spectroscopy with a magnetic resonance imaging apparatus is set, and
 the first training label is a mark indicative of a position of the measurement voxel.   
     
     
         9 . The apparatus of machine learning according to  claim 5 , wherein the processing circuitry is configured to:
 calculate, from the first training label, a calibration parameter for calibrating individual characteristics in label assignment by the first user; and   train, based on the first input training data, the first training label, and the calibration parameter, the first calibration model configured to receive input data as input, and to output as the calibration data a calibration parameter corresponding to the input data.   
     
     
         10 . The apparatus of machine learning according to  claim 5 , wherein the processing circuitry is configured to train, based on the first input training data, the first training label, and a correct label with respect to the first input training data, the first calibration model that receives, as input, input data and a label assigned by the first user to the input data and outputs, as the calibration data, a correct label for the input data. 
     
     
         11 . The apparatus of machine learning according to  claim 5 , wherein the processing circuitry is configured to.
 determine a reliability of the first training label with respect to the first input training data; and   train the first calibration model that receives, as input, input data and a label assigned by the first user to the input data based on the first input training data, the first training label, and the reliability, and outputs a reliability of the label with respect to the input data.   
     
     
         12 . The apparatus of machine learning according to  claim 5 , wherein the processing circuitry is configured to train the target model based on a combination of the first processing data, the first processing label, and the first calibration model and a combination of second processing data, a second processing label assigned by a second user to the second processing data, and a second calibration model for calibrating individual characteristics in label assignment by the second user. 
     
     
         13 . The apparatus of machine learning according to  claim 5 , wherein the processing circuitry is configured to train the target model based on a combination of the first processing data, the first processing label assigned by the first user to the first processing data under a first assignment condition, and the first calibration model, and a combination of second processing data, a second processing label assigned by the first user to the second processing data under a second assignment condition,
 the first calibration model is a calibration model for calibrating individual characteristics in label assignment by the first user under the first assignment condition, and   the second calibration model is a calibration model for calibrating individual characteristics in label assignment by the first user under the second assignment condition.   
     
     
         14 . The apparatus of machine learning according to  claim 1 , wherein the target model is a machine learning model trained in such a manner as to receive input processing data as input and to output prediction data corresponding to the input processing data. 
     
     
         15 . The apparatus of machine learning according to  claim 1 , wherein the processing circuitry is configured to train an untrained first calibration model by copying a training parameter of a trained calibration model corresponding to a user other than the first user to the untrained first calibration model, and training a training parameter of the untrained first calibration model based on the first input training data, the first training label, and the calibration data. 
     
     
         16 . The apparatus of machine learning according to  claim 1 , wherein the first calibration model includes a plurality of calibration models respectively corresponding to a plurality of users other than the first user, and an addition layer that weight-adds a plurality of calibration data pieces from the calibration models in accordance with a weight trained for calibration of label assignment by the first user, and outputs the calibration data pieces. 
     
     
         17 . A machine learning method comprising: training, by using a calibration model that receives, as input, processing data and a processing label assigned by a user to the processing data, and outputs calibration data relating to calibration of individual characteristics in label assignment by the user, a target model based on at least the processing data and the calibration data or a calibration label having individual characteristics calibrated using the calibration data. 
     
     
         18 . An inference apparatus comprising processing circuitry configured to make an inference with a model trained using a calibration model that receives, as input, processing data and a processing label assigned by a user to the processing data, and outputs calibration data relating to calibration of individual characteristics in label assignment by the user, based on at least the processing data and the calibration data or a calibration label having individual characteristics calibrated using the calibration data.